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Real-time image processing for vision-based weld seam tracking in robotic GMAW


Image capturing and processing is important in using vision sensor to effectively track the weld seam and control the weld quality in robotic gas metal arc welding (GMAW). Using vision techniques to track weld seam, the key is to acquire clear weld images and process them accurately. In this paper, a method for real-time image capturing and processing is presented for the application in robotic seam tracking. By analyzing the characteristic of robotic GMAW, the real-time weld images are captured clearly by the passive vision sensor. Utilizing the main characteristics of the gray gradient in the weld image, a new improved Canny edge detection algorithm was proposed to detect the edges of weld image and extract the seam and pool characteristic parameters. The image processing precision was further verified by using the random welding experiments. Results showed that the precision range of the image processing can be controlled to be within ±0.3 mm in robotic GMAW, which can meet the requirement of real-time seam tracking.

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  1. Fang ZJ, Xu D, Tan M (2013) Vision-based initial weld point positioning using the geometric relationship between two seams. Int J Adv Manuf Technol 66:1535–1543

    Article  Google Scholar 

  2. Gu WP, Xiong ZY, Wan W (2013) Autonomous seam acquisition and tracking system for multi-pass welding based on vision sensor. Int J Adv Manuf Technol 69:451–460

    Article  Google Scholar 

  3. Kawahara M (1983) Tracking control system using image sensor for arc welding. Automatica 19:357–363

    Article  Google Scholar 

  4. Chen SB, Zhang Y, Qiu T, Lin T (2003) Robotic welding systems with vision sensing and self-learning neuron control of Arc weld dynamic process. J Intell Robot Syst 36:191–208

    Article  Google Scholar 

  5. Shen HY, Ma HB, Lin T, Chen SB (2007) Research on weld pool control of welding robot with computer vision. Ind Robot 34:467–475

    Article  Google Scholar 

  6. Kong M, Chen SB (2009) Al alloy weld pool control of welding robot with passive vision. Sens Rev 29:28–37

    Article  Google Scholar 

  7. Xu YL, Yu HW, Zhong JY, Lin T, Chen SB (2012) Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor. J Mater Process Technol 212:1654–1662

    Article  Google Scholar 

  8. Xu YL, Yu HW, Zhong JY, Lin T, Chen SB (2012) Real-time image capturing and processing of seam and pool during robotic welding process. Int J: Ind Robot 39:513–523

    Google Scholar 

  9. Kim JW, Na SJ (1993) A self-organizing fuzzy control approach to arc sensor for weld joint tracking in gas metal arc welding of butt joints. Weld Res Suppl 2:60–66

    Google Scholar 

  10. Shi YH, Yoo WS, Na SJ (2006) Mathematical modeling of rotational arc sensor in GMAW and its applications to seam tracking and endpoint detection. Sci Tech Weld Join 11:723–730

    Article  Google Scholar 

  11. Bae KY, Lee TH, Ahn KC (2002) An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe. J Mater Process Technol 120:458–465

    Article  Google Scholar 

  12. Dinham M, Fang G (2013) Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding. Int J Comput Integr Manuf 29:288–301

    Article  Google Scholar 

  13. Ye Z, Fang G, Chen SB, Dinham M (2013) A robust algorithm for weld seam extraction based on prior knowledge of weld seam. Sens Rev 33:125–133

    Article  Google Scholar 

  14. Ye Z, Fang G, Chen SB, Zou JJ (2013) Passive vision based seam tracking system for pulse-MAG welding. Int J Adv Manuf Technol 67:1987–1996

    Article  Google Scholar 

  15. Bouguet JY (2010) Camera calibration toolbox for Matlab.

  16. Zhang ZY (2000) A flexible new technique for camera calibration. IEEE Transction Pattern Anal Mach Intell 22:1330–1334

    Article  Google Scholar 

  17. Canny J (1986) A computational approach to edge detection. IEEE Transction Pattern Anal Mach Intell 8:679–698

    Article  Google Scholar 

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Correspondence to Yanling Xu.

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Xu, Y., Fang, G., Chen, S. et al. Real-time image processing for vision-based weld seam tracking in robotic GMAW. Int J Adv Manuf Technol 73, 1413–1425 (2014).

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  • Robotic GMAW
  • Image capturing
  • Image processing
  • Passive vision sensor
  • Seam tracking